↓ Skip to main content

Model-based prediction of human hair color using DNA variants

Overview of attention for article published in Human Genetics, January 2011
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#27 of 2,512)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
2 news outlets
blogs
4 blogs
twitter
3 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
93 Dimensions

Readers on

mendeley
205 Mendeley
citeulike
7 CiteULike
Title
Model-based prediction of human hair color using DNA variants
Published in
Human Genetics, January 2011
DOI 10.1007/s00439-010-0939-8
Pubmed ID
Authors

Wojciech Branicki, Fan Liu, Kate van Duijn, Jolanta Draus-Barini, Ewelina Pośpiech, Susan Walsh, Tomasz Kupiec, Anna Wojas-Pelc, Manfred Kayser

Abstract

Predicting complex human phenotypes from genotypes is the central concept of widely advocated personalized medicine, but so far has rarely led to high accuracies limiting practical applications. One notable exception, although less relevant for medical but important for forensic purposes, is human eye color, for which it has been recently demonstrated that highly accurate prediction is feasible from a small number of DNA variants. Here, we demonstrate that human hair color is predictable from DNA variants with similarly high accuracies. We analyzed in Polish Europeans with single-observer hair color grading 45 single nucleotide polymorphisms (SNPs) from 12 genes previously associated with human hair color variation. We found that a model based on a subset of 13 single or compound genetic markers from 11 genes predicted red hair color with over 0.9, black hair color with almost 0.9, as well as blond, and brown hair color with over 0.8 prevalence-adjusted accuracy expressed by the area under the receiver characteristic operating curves (AUC). The identified genetic predictors also differentiate reasonably well between similar hair colors, such as between red and blond-red, as well as between blond and dark-blond, highlighting the value of the identified DNA variants for accurate hair color prediction.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 205 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
Switzerland 2 <1%
Brazil 1 <1%
Italy 1 <1%
Thailand 1 <1%
China 1 <1%
Spain 1 <1%
Uruguay 1 <1%
Greece 1 <1%
Other 0 0%
Unknown 193 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 20%
Researcher 37 18%
Student > Master 33 16%
Student > Bachelor 27 13%
Unspecified 16 8%
Other 51 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 94 46%
Biochemistry, Genetics and Molecular Biology 49 24%
Unspecified 21 10%
Medicine and Dentistry 18 9%
Arts and Humanities 5 2%
Other 18 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 46. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 October 2018.
All research outputs
#338,933
of 12,830,933 outputs
Outputs from Human Genetics
#27
of 2,512 outputs
Outputs of similar age
#3,845
of 146,407 outputs
Outputs of similar age from Human Genetics
#1
of 18 outputs
Altmetric has tracked 12,830,933 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,512 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 146,407 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.